Chen Meng, Yin Zhixiang
School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China.
Front Cell Dev Biol. 2022 May 11;10:888859. doi: 10.3389/fcell.2022.888859. eCollection 2022.
Cardiotocography (CTG) recorded fetal heart rate and its temporal relationship with uterine contractions. CTG intelligent classification plays an important role in evaluating fetal health and protecting fetal normal growth and development throughout pregnancy. At the feature selection level, this study uses the Apriori algorithm to search frequent item sets for feature extraction. At the level of the classification model, the combination model of AdaBoost and random forest with the highest classification accuracy is finally selected by comparing various models. The suspicious class data in the CTG data set affect the overall classification accuracy. The number of suspicious class data is predicted by the multi-model ensemble method. Finally, the data set is fused from three classifications to two classifications. The classification accuracy is 0.976, and the AUC is 0.98, which significantly improves the classification effect. In conclusion, the method used in this study has high accuracy in model classification, which is helpful to improve the accuracy of fetal abnormality detection.
胎心监护（CTG）记录胎儿心率及其与子宫收缩的时间关系。CTG智能分类在评估胎儿健康以及保障整个孕期胎儿正常生长发育方面发挥着重要作用。在特征选择层面，本研究使用Apriori算法搜索频繁项集以进行特征提取。在分类模型层面，通过比较各种模型最终选择了分类准确率最高的AdaBoost和随机森林组合模型。CTG数据集中的可疑类别数据会影响整体分类准确率。采用多模型集成方法预测可疑类别数据的数量。最后，将数据集从三类融合为两类。分类准确率为0.976，AUC为0.98，显著提高了分类效果。总之，本研究采用的方法在模型分类方面具有较高的准确率，有助于提高胎儿异常检测的准确性。
